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Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

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Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

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Nombre: Erhard Næss-Schmi ...
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dc.contributor.author Naess-Schmidt, Erhard es_ES
dc.contributor.author Tietze, Anna es_ES
dc.contributor.author Blicher, Jakob Udby es_ES
dc.contributor.author Petersen, Mikkel es_ES
dc.contributor.author Mikkelsen, Irene K. es_ES
dc.contributor.author Coupe, Pierrick es_ES
dc.contributor.author Manjón Herrera, José Vicente es_ES
dc.contributor.author Eskildsen, Simon Fristed es_ES
dc.date.accessioned 2017-05-29T07:39:09Z
dc.date.available 2017-05-29T07:39:09Z
dc.date.issued 2016-11
dc.identifier.issn 1861-6410
dc.identifier.uri http://hdl.handle.net/10251/81872
dc.description.abstract Purpose In both structural and functional MRI, there is a need for accurate and reliable automatic segmentation of brain regions. Inconsistent segmentation reduces sensitivity and may bias results in clinical studies. The current study compares the performance of publicly available segmentation tools and their impact on diffusion quantification, emphasizing the importance of using recently developed segmentation algorithms and imaging techniques. Methods Four publicly available, automatic segmentation methods (volBrain, FSL, FreeSurfer and SPM) are compared to manual segmentation of the thalamus and hippocampus imaged with a recently proposed T1-weighted MRI sequence (MP2RAGE). We evaluate morphometric accuracy on 22 healthy subjects and impact on diffusivity measurements obtained from aligned diffusion-weighted images on a subset of 10 subjects. Results Compared to manual segmentation, the highest Dice similarity index of the thalamus is obtained with volBrain using a local library (M = 0.913, SD = 0.014) followed by volBrain using an external library (M = 0.868, SD = 0.024), FSL (M = 0.806, SD = 0.034), FreeSurfer (M = 0.798, SD = 0.049) and SPM (M = 0.787, SD = 0.031). The same order is found for hippocampus with volBrain local (M = 0.892, SD = 0.016), volBrain external (M = 0.859, SD = 0.014), FSL (M = 0.808, SD = 0.017), FreeSurfer (M = 0.771, SD = 0.023) and SPM (M = 0.735, SD = 0.038). For diffusivity measurements, volBrain provides values closest to those obtained from manual segmentations. volBrain is the only method where FA values do not differ significantly from manual segmentation of the thalamus. Conclusions Overall we find that volBrain is superior in thalamus and hippocampus segmentation compared to FSL, FreeSurfer and SPM. Furthermore, the choice of segmentation technique and training library affects quantitative results from diffusivity measures in thalamus and hippocampus. es_ES
dc.description.sponsorship This work was funded in part by MINDLab UNIK initiative at Aarhus University, funded by the Danish Ministry of Science, Technology and Innovation, Grant Agreement Number 09065250, partly by the Spanish grant TIN2013-43457-R from the Ministerio de Economia competitividad and with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02) by funding HL-DTI grant, Cluster of excellence CPU, LaBEX TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project "Defi ImagIn". en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof International Journal of Computer Assisted Radiology and Surgery es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject MRI es_ES
dc.subject Segmentation es_ES
dc.subject Hippocampus es_ES
dc.subject Thalamus es_ES
dc.subject MP2RAGE es_ES
dc.subject Diffusion-weighted imaging es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11548-016-1433-0
dc.relation.projectID info:eu-repo/grantAgreement/Ministry of Higher Education and Science//9065250/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-10-LABX-0057/FR/Translational Research and Advanced Imaging Laboratory/TRAIL/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-10-IDEX-0003/FR/Initiative d’excellence de l’Université de Bordeaux/IDEX BORDEAUX/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2013-43457-R/ES/CARACTERIZACION DE FIRMAS BIOLOGICAS DE GLIOBLASTOMAS MEDIANTE MODELOS NO-SUPERVISADOS DE PREDICCION ESTRUCTURADA BASADOS EN BIOMARCADORES DE IMAGEN/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Naess-Schmidt, E.; Tietze, A.; Blicher, JU.; Petersen, M.; Mikkelsen, IK.; Coupe, P.; Manjón Herrera, JV.... (2016). Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification. International Journal of Computer Assisted Radiology and Surgery. 11(11):1979-1991. https://doi.org/10.1007/s11548-016-1433-0 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://link.springer.com/article/10.1007/s11548-016-1433-0 es_ES
dc.description.upvformatpinicio 1979 es_ES
dc.description.upvformatpfin 1991 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 11 es_ES
dc.relation.senia 329838 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Ministry of Higher Education and Science, Dinamarca es_ES
dc.contributor.funder Agence Nationale de la Recherche, Francia es_ES
dc.contributor.funder Centre National de la Recherche Scientifique, Francia es_ES
dc.contributor.funder Aarhus University
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